Performing versatile mobile manipulation actions in human-centered environments requires highly sophisticated software frameworks that are flexible enough to handle special use cases, yet general enough to be applicable across different robotic systems, tasks, and environments. This paper presents a comprehensive memory-centered, affordance-based, and modular uni- and multi-manual grasping and mobile manipulation framework, applicable to complex robot systems with a high number of degrees of freedom such as humanoid robots. By representing mobile manipulation actions through affordances, i.e., interaction possibilities of the robot with its environment, we unify the autonomous manipulation process for known and unknown objects in arbitrary environments. Our framework is integrated and embedded into the memory-centric cognitive architecture of the ARMAR humanoid robot family. This way, robots can not only interact with the physical world but also use common knowledge about objects, and learn and adapt manipulation strategies. We demonstrate the applicability of the framework in real-world experiments, including grasping known and unknown objects, object placing, and semi-autonomous bimanual grasping of objects on two different humanoid robot platforms.

翻译：在人机共融环境中执行多功能移动操作需要高度复杂的软件框架，这些框架需具备足够灵活性以处理特殊用例，同时保持通用性以适用于不同机器人系统、任务和环境。本文提出了一种全面的、以记忆为中心、基于可供性且模块化的单/多臂抓取与移动操作框架，可应用于自由度较高的复杂机器人系统（如人形机器人）。通过将移动操作行为表征为可供性（即机器人与环境交互的可能性），我们统一了任意环境中已知与未知物体的自主操作流程。该框架被集成并嵌入到ARMAR人形机器人系列的以记忆为中心的认知架构中，使得机器人不仅能与物理世界交互，还能利用关于物体的常识知识，学习并适应操作策略。我们通过真实世界实验验证了该框架的适用性，包括在两种不同人形机器人平台上进行的已知/未知物体抓取、物体放置及半自主双臂抓取操作。